Multi-racial facial recognition system found to be more accurate
06 November 2017 17:52 GMT

A team of researchers has found that a face recognition database trained with people of different races was more accurate in identifying them.

The team, from the UK's University of Surrey, has developed a 3D morphing face model that has 'learned' from different racial faces.

Surrey's Centre for Vision, Speech and Signal Processing (CVSSP) found that the use of multi-racial 3D face models improves accuracy when trying to recognise people. It also found that the team's aging effect technology - which is used to identify individuals after a long period of time has passed - is more precise when you use a model that is taught to learn different races.

Lead author of the paper Dr Zhenhua Feng from CVSSP said: "It's safe to say that facial recognition technology is slowly becoming more prevalent in our daily lives. We need to make sure it's as accurate as possible, so people can trust the technology. We have found that our model that understands black, white and Asian faces is far more accurate at recognising 2D faces than the typical all-in-one models used today."

Dr Feng has recently won a prestigious European Biometric Industry Award for his work around facial landmark localisation and he is part of a team at CVSSP that is working on a £6m project for the Engineering and Physical Sciences Research Council to make facial recognition ubiquitous across the country.

Professor Josef Kittler, Distinguished Professor at the University of Surrey and founder of CVSSP, said: "We believe that facial recognition technology will be a force for good. It will help us protect our possessions, provide better security for our data and keep us safe from harm. However, the matter of accuracy is something we all have to be mindful of and that is what we are working on improving at CVSSP.

"Dr Feng's project and the wider work we are doing at the Centre is focused on improving the accuracy of facial recognition technology, even in extreme cases where the resolution of the corresponding image is compromised, or in cases where people may try to trick a system."